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All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Simulation techniques for estimating error in the classification of normal patterns

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Whitsitt, S. J.↗
DOE OSTI2023

Pore Structure and Wettability of Bossier Shale, East Texas, United States: Insights from Integrated Porosimetry, Scattering, and Imbibition Approaches

In this study, ascertaining the pore geometry and wettability characteristics of tight shales is of great significance for revealing the mechanisms of occurrence, migration, and production of shale resources. Taking the Bossier Shale collected from Well A, East Texas, USA, as an example, the pore geometry and wettability behavior in a broad nm-& μm scale pore spectrum were quantified based on integrated techniques, including water immersion porosimetry (WIP), mercury intrusion porosimetry(MIP), (ultra)small-angle X-ray scattering [(U)SAXS], contact angle, and liquid spontaneous imbibition (SI). Mainly owing to differences in the sample sizes used, data interpretation, as well as the detectable pore type (e.g., connected or not) and pore diameter ranges among different methods, sample porosities derived from WIP, MIP, and (U)SAXS were quite different, which range from 7.70 to 13.34%, 5.12 to 9.52%, and 2.48 to 6.44%, respectively. Therefore, a comparison of the porosities derived from different methods must be handled with utmost care. Additionally, although (U)SAXS is an effective technique for detecting both connected and non-connected pores, the fraction of non-connected pores could be underestimated by directly comparing the pore size distribution derived from (U)SAXS and MIP tests. This is because the sample sizes used for and pore information reflected by these two methods are different. Furthermore, the studied Bossier Shales exhibited various contact angle values and imbibition behaviors when using differently polarized liquids, revealing their mixed-wet characteristics. Comprehensively considering the differences in contact angle, imbibition slope, and imbibed liquid volume, three sub-categories of wettability behavior, with respect to more oil-wet, more water-wet, and intermediate mixed-wet, were qualitatively identified.

(U)SAXS↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

The Genesis Non-flight Implant Sample Archive

Introduction: For the past 20 years the Genesis science community has been analyzing solar wind captured in sample collectors now curated at Johnson Space Center (JSC). Non-flown flight like collector materials (non-flight) considered reference material was always allocated by the JSC Genesis team for testing experimental procedures. However, to expedite Genesis science, distribution of non-flown reference materials were also provided by, D. S. Burnett (Genesis PI, Caltech) and A. J. G. Jurewicz (ASU) who were funded to aid all Genesis PIs by providing engineering test materials archived at Caltech pre-flight as well as ion implants into these materials [1]. Implanted materials (non-flight reference, flight-like semiconductor materials, and NIST SRM glass) received from the Caltech collection during a pilot project are being used to develop an accession procedure for the purpose of making these materials available to researchers in the future. These implant samples are important because they can be utilized as measurement standards [2]. This is a work in progress and this work reports progress in documentation and imaging. The pilot program received 63 implanted reference materials with known implanted ions. Genesis Non-Flight Database: The existing non-flight material database was modified to allow for the description of implant sessions and to allow correlation of the implant information to individual specimens. An implant session has three main components: a known ion, a known dose (ion/cm2), and a known energy (keV). Procedure: Laboratory Procedure. Upon receiving non-flight analytical standards (independently calibrated implants), or implant samples (nominally calibrated implants), they will be imaged in their current containers to capture any information written before being brought into the Genesis Sample Laboratory. Documentation is provided by the donor and used for correlating the data between the implant sessions and implant samples. This information is not verified by Genesis Curation. The microscope used to image samples in the laboratory has an automated stage with a stainless-steel plate to hold samples and a camera that utilizes the software Surveyor to take photomosaic images of the implant samples. The camera attached to the microscope is a QICAM High Performance IEEE 1394 FireWire Digital CCD Camera. This setup, shown in Fig. 1, allows for photomosaics of small-scale samples with a 10x objective or 25x objective. Examples of images taken with this microscope setup are shown in Fig.2 and Fig. 3. After imaging, samples get stored in a clean fluoroware container or polypropylene vial depending on sample size for long term storage. Database Procedure. The Genesis Non-Flight Database currently allows for the addition of implant sessions. The required fields for adding an implant session are the implant session ion, the implant session dose, and the implant session energy. There are also fields for the date and vendor of the sessions as well as a field for if the session was calibrated or not. Once an implant session has been inserted into the database, additional documentation from the session can be attached. The database will automatically assign an Implant Session Number. After inserting an implant session, the implanted samples can be inserted. The fields required for an implant sample are the material of the sample and the size of the sample. The Generic name for the implant sample with be automatically generated by the database once it has been inserted. Implant sample numbers begin with 3X to distinguish from flown Genesis sample numbers. There are also fields for processors to add the location (room, cabinet, tray) and container type (fluoroware, polypropylene vial). Once an implant session and an associated sample(s) have been inserted into the database the sample can be then tied to all associated implant sessions. An implant sample can be tied to multiple implant sessions. Future work: For curation, future work consists of creating an online catalogue for implant samples as well as updating the database with more samples as we receive them. For the scientific community these implant samples have many potential uses. For Genesis research, they are directly applicable for testing and, in some cases, as analytical standards. For non-Genesis research, implants (especially calibrated implants) could be used for calibrating other, unique planetary materials [1].

C D Calva↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2020

Using yttrium as an indicator to estimate total rare earth element concentration: a case study of anthracite-associated clays from northeastern Pennsylvania

Abstract This study demonstrated using yttrium (Y) as an indicator to estimate the total rare earth element and Y contents (REY) in coal-associated samples and to facilitate selection of samples with high REY assays in a fast and inexpensive manner. More than 10 anthracite-associated samples were collected from each of three Pennsylvanian sites (sites B, J and C) based on Thorium gamma ray logging suggesting high REY content. Several samples from each site were analyzed by ICP-MS to determine the rare earth distribution patterns and to establish the site-specific linear equations of Y and REY. The Y contents of the remaining samples were measured by a portable X-ray fluorescence analyzer, and the REY values were estimated based on the site-specific linear equation developed earlier. R-squared values above 0.70 were obtained for all the estimation equations from all three sites on both a whole sample basis and an ash basis. Previously, ash content has been widely used as an indicator of high REY content. This may not be applicable for a specific site. Site B in this study is an example where ash contents could not be statistically correlated with REY, so using Y for estimation is more applicable. The demonstrated sample screening process is suitable for samples from sites that share more similar distribution patterns (either MREY or LREY or HREY) as well as for samples from sites that share multiple distribution patterns (LREY/MREY/HREY) depending on the desirable accuracy. The demonstrated process lowers the analytical cost from $70 to 80 dollars per sample to $10–15 per sample while significantly reducing the processing time and acid consumption for ICP digestion. This is particularly true when a relatively large sample size is involved, for example, 100 samples from one site analyzed by ICP-MS/OES.

Yang, Xiaojing↗
DOE OSTI2023

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

NASA GRC and MSFC Space-Plasma Arc Testing Procedures

Tests of arcing and current collection in simulated space plasma conditions have been performed at the NASA Glenn Research Center (GRC) in Cleveland, Ohio, for over 30 years and at the Marshall Space Flight Center (MSFC) in Huntsville, Alabama, for almost as long. During this period, proper test conditions for accurate and meaningful space simulation have been worked out, comparisons with actual space performance in spaceflight tests and with real operational satellites have been made, and NASA has achieved our own internal standards for test protocols. It is the purpose of this paper to communicate the test conditions, test procedures, and types of analysis used at NASA GRC and MSFC to the space environmental testing community at large, to help with international space-plasma arcing-testing standardization. To be discussed are: 1.Neutral pressures, neutral gases, and vacuum chamber sizes. 2. Electron and ion densities, plasma uniformity, sample sizes, and Debuy lengths. 3. Biasing samples versus self-generated voltages. Floating samples versus grounded. 4. Power supplies and current limits. Isolation of samples from power supplies during arcs. 5. Arc circuits. Capacitance during biased arc-threshold tests. Capacitance during sustained arcing and damage tests. Arc detection. Prevention sustained discharges during testing. 6. Real array or structure samples versus idealized samples. 7. Validity of LEO tests for GEO samples. 8. Extracting arc threshold information from arc rate versus voltage tests. 9. Snapover and current collection at positive sample bias. Glows at positive bias. Kapon (R) pyrolisis. 10. Trigger arc thresholds. Sustained arc thresholds. Paschen discharge during sustained arcing. 11. Testing for Paschen discharge threshold. Testing for dielectric breakdown thresholds. Testing for tether arcing. 12. Testing in very dense plasmas (ie thruster plumes). 13. Arc mitigation strategies. Charging mitigation strategies. Models. 14. Analysis of test results. Finally, the necessity of testing will be emphasized, not to the exclusion of modeling, but as part of a complete strategy for determining when and if arcs will occur, and preventing them from occurring in space.

Ferguson, Dale C.↗
NASA NTRS2005

NASA GRC and MSFC Space-Plasma Arc Testing Procedures

Tests of arcing and current collection in simulated space plasma conditions have been performed at the NASA Glenn Research Center (GRC) in Cleveland, Ohio, for over 30 years and at the Marshall Space flight Center (MSFC) for almost as long. During this period, proper test conditions for accurate and meaningful space simulation have been worked out, comparisons with actual space performance in spaceflight tests and with real operational satellites have been made, and NASA has achieved our own internal standards for test protocols. It is the purpose of this paper to communicate the test conditions, test procedures, and types of analysis used at NASA GRC and MSFC to the space environmental testing community at large, to help with international space-plasma arcing testing standardization. To be discussed are: 1. Neutral pressures, neutral gases, and vacuum chamber sizes. 2. Electron and ion densities, plasma uniformity, sample sizes, and Debye lengths. 3. Biasing samples versus self-generated voltages. Floating samples versus grounded. 4. Power supplies and current limits. Isolation of samples from power supplies during arcs. Arc circuits. Capacitance during biased arc-threshold tests. Capacitance during sustained arcing and damage tests. Arc detection. Preventing sustained discharges during testing. 5. Real array or structure samples versus idealized samples. 6. Validity of LEO tests for GEO samples. 7. Extracting arc threshold information from arc rate versus voltage tests. 8 . Snapover and current collection at positive sample bias. Glows at positive bias. Kapton pyrolization. 9. Trigger arc thresholds. Sustained arc thresholds. Paschen discharge during sustained arcing. 10. Testing for Paschen discharge thresholds. Testing for dielectric breakdown thresholds. Testing for tether arcing. 11. Testing in very dense plasmas (ie thruster plumes). 12. Arc mitigation strategies. Charging mitigation strategies. Models. 13. Analysis of test results. Finally, the necessity of testing will be emphasized, not to the exclusion of modeling, but as part of a complete strategy for determining when and if arcs will occur, and preventing them from occurring in space.

Ferguson, Dale C.a↗
NASA NTRS1995

Designing for Small Volume Assembly of Advanced Electronics Packages

We describe a general methodology to Design for Producibility and Reliability (DFPAR) for very small volume production runs. In cases where the entire volume for fabrication is less than five products, traditional Statistical Process Control (SPC) is inadequate due to reliance on statistics of much larger volumes and the Central Limit Theorem. Data acquisition for process parameter estimation from such a small sample size is difficult; however, it is critical to producing high reliability product.

Central Limit Theorem statistics high reliability ↗
NASA NTRS2001

Application of Computer Tomography for Life Detection

Perhaps one of the most fundamentally difficult challenges facing those who would search for life is that of scale determination. Spatial scales of life on Earth range over more than 15 orders of magnitude in mass and volume, and more than 8 orders of magnitude in 2 dimensional space. If the distribution of life is sparse in comparison to the background on which it is found, then the choice of the right scale is critical to finding that life. But how does one identify the proper scale? To put this in other words, how does one recognize the "haystacks" in which the needles (biosignatures and evidence of life) might be most profitably searched for? The problem is further exacerbated when conditions get extreme because much of the life moves from the clement surface environment into the pores and more clement environments inside of rocks, minerals and soils. Once encased in their lithic homes, these microbes become nearly impossible to study by standard techniques because of the opacity of the rocks. It is this problem that we propose to address in the work proposed here. Computer Tomography (CT) has been a very valuable tool in medicine, where the best resolution available has typically been of the order of about 0.5 mm. However, to adapt the approach for life detection of microbial endoliths, the resolution needs to be moved to the micrometer and even submicrometer levels. Thus for the studies proposed here, we begin with a commercially available instrument that can yield resolution of approximately 10 micrometers. The rational for this is twofold: first, this is the "state of the art" in laboratory instruments; and second, that while the usual size of a microbial cell is about 1 micron, microorganisms tend to live in communities that usually exceed the 10 micrometer size range. The resolution also depends on the sample size itself, so having a small lab instrument into which small samples can be placed will be beneficial to the resolution. We have now used several different CT systems, beginning with the medical scanners (Arcadia CT group) for the detection of layered communities in sandstone rocks from Antarctica. Even this crude instrument was able to point to the areas of the rock that were dominated by microbial populations - this provides the critical first information that says, "Go back and look at these sites with other methods." We showed that without sample preparation or destruction it was possible to gain knowledge as to the presence of density differences suggestive of life.

Tsapin, A.↗
DOE OSTI2026

Characterization of Electronic Stress-Induced Changes in Multilayer MoS 2

Transition metal dichalcogenides like molybdenum disulfide (MoS 2 ) are compelling for next-generation electronic devices. In this work, we investigate the impact of electronic stress on MoS 2 to illustrate that observational and phenomenological information on multiple devices can be useful to describe changes in the device, and caution against the rationalization of paltry results as representative or correlative to device behavior. Here, we stress MoS 2 by applying a sustained 20 V DC bias to study the material’s response. Post-stress electronic characterization revealed nonuniform shifts in current–voltage (I–V) behavior alongside microscale changes. Complementary mechanical, spectroscopic, and scanning microwave impedance measurements showed that stress-induced features locally modulate stiffness, surface potential, Raman intensity, and charge carrier density. We correlated I–V behavior with morphological features (wrinkles, tears, folds, height) and device-level geometry (MoS 2 overlap with electrodes, channel area, contact length) on 50 test structures across five chips to move beyond anecdotal conclusions. We found no universal correlations before DC stress. However, device-level geometry was correlated with I–V behavior after DC stress, suggesting that electrode contacts play a more dominant role than morphology in determining performance. Delamination and thinning induced by DC stress led to localized reductions in charge carrier density within the affected regions. Further, delamination and thinning appear to map to I–V device performance in a few samples, but the correlation is lost when a larger sample size is considered. This suggests significant sample-to-sample variability in surface electronic states of the test structures. We also discuss how environmental factors introduced during fabrication may contribute to the observed heterogeneous device response. Progress will require high-resolution, multimodal analysis across many samples constructed under controlled, clean conditions. By building data sets that capture variability, we can better identify the true drivers of performance.

36 MATERIALS SCIENCE↗
NASA NTRS1971

A lightweight, high output soil sampler

Sampler is useful on or under earth's surface or on sea bottom. Larger sample amount is obtained relative to sampler size and weight and limited particle size sample material is continuously delivered. Silicone rubber linear in transport tube nearly eliminates grinding or particulate processing during sampling, and reduces required torque.

Howard, E. A.↗
NASA NTRS1997

Computer based imaging and analysis of root gravitropism

Two key issues in studies of the nature of the gravitropic response in roots have been the determination of the precise pattern of differential elongation responsible for downward bending and the identification of the cells that show the initial motor response. The main approach for examining patterns of differential growth during root gravitropic curvature has been to apply markers to the root surface and photograph the root at regular intervals during gravitropic curvature. Although these studies have provided valuable information on the characteristics of the gravitropic motor response in roots, their labor intensive nature limits sample size and discourages both high frequency of sampling and depth of analysis of surface expansion data. In this brief review we describe the development of computer-based video analysis systems for automated measurement of root growth and shape change and discuss some key features of the root gravitropic response that have been revealed using this methodology. We summarize the capabilities of several new pieces of software designed to measure growth and shape changes in graviresponding roots and describe recent progress in developing analysis systems for studying the small, but experimentally popular, primary roots of Arabidopsis. A key finding revealed by such studies is that the initial gravitropic response of roots of maize and Arabidopsis occurs in the distal elongation zone (DEZ) near the root apical meristem, not in the main elongation zone. Another finding is that the initiation of rapid elongation in the DEZ following gravistimulation appears to be related to rapid membrane potential changes in this region of the root. These observations have provided the incentive for ongoing studies examining possible links between potential growth modifying factors (auxin, calcium, protons) and gravistimulated changes in membrane potential and growth patterns in the DEZ.

NASA Discipline Plant Biology↗
NASA NTRS2005

Nutrititional Status Assessment of International Space Station Crew Members

Defining optimal nutrient requirements is imperative to ensure crew health on long-duration space exploration missions. To date, nutrient requirement data have been extremely limited because of small sample sizes and difficulties associated with collecting biological samples. In this study, we examined changes in body composition, bone metabolism, hematology, general blood chemistry, and blood levels of selected vitamins and minerals after long-duration (128-195 d) space flight aboard the International Space Station. Crew members consumed an average of 80% of the recommended energy intakes, and on landing day their body weight had decreased (P=0.051). After flight, hematocrit was less, and serum femtin was greater than before flight (P<0.01). Serum iron, ferritin saturation, and transferrin had decreased after flight. The finding that other acute-phase proteins, including ceruloplasmin, retinol binding protein, transthyretin, and albumin were not changed after flight suggests that the changes in iron metabolism may not be strictly due to an inflammatory response. Urinary 8- hydroxy-2'-deoxyguanosine concentration was greater and superoxide dismutase was less after flight, indicating that oxidative damage had increased (P<0.05). Despite the reported use of vitamin D supplements during flight, serum 25-hydroxyvitamin D was significantly decreased after flight (P<0.01). Bone resorption was increased after flight, as indicated by several urinary markers of bone resorption. Bone formation, assessed by serum concentration of bone-specific alkaline phosphatase, was elevated only in crew members who landed in Russia, probably because of the longer time lapse between landing and sample collection. These data provide evidence that bone loss, compromised vitamin D status, and oxidative damage remain critical concerns for long-duration space flight.

Smith, S. M.↗
DOE OSTI2023

Defect Characterization of Monocrystalline Silicon Solar Cells with Polysilicon Passivated Contact Using Electrically-Detected Magnetic Resonance (EDMR) Spectroscopy

As the c-Si based solar cell efficiencies are approaching over 26%, it is becoming critical to characterize the low concentrations of the defects – as low as 10^10-10^11 cm-3 (for e.g., iron contamination in high-lifetime Ga-doped wafers3 and n-type wafers), and further reduce them. Also, atomistic level understanding of the mechanisms of the low concentration process-induced-defects and reliability limiting defects (such as light and elevated temperature induced degradation, surface passivation degradation) is needed to design the mitigation strategies. The conventional characterization techniques are limited due to their detection limitations. Some of the techniques based on lifetime spectroscopies can still be used for low concentration characterization however, they are based on estimations and theoretical models and hence, indirect and cannot fully reveal information about the microscopic mechanism of the defects. Thus, we present the application of an ultrasensitive magnetic resonance-based technique for the direct spectroscopic detection of the defects in Si PV - electrically detected magnetic resonance (EDMR). In this work, we aim to focus on establishing a process flow for fabrication of minicells with (miniature replica of the larger-area cells) and setting up the routine for EDMR measurements on them with the EDMR instrumentation capability at NREL. For the EDMR measurements, sample size is limited by the dimensions of sample holder tube (width less than 3.2 mm, active area - 20 mm). Thus, we have designed c-Si based minicells with polysilicon (poly-Si) passivated contacts same as the larger-area cells that we fabricate in our group at NREL. We also modified our minicell process flow for fabricating the textured minicells for preserving the texture during processing and taking care of the laser-ablation edge damage which can significantly affect the performance of such small devices. We have achieved comparable performance on these newly fabricated minicells as that of our 4 cm2 devices with same structure (comparable VOC, JSC, FF). We also conducted EDMR measurements on the minicells and observed a distinct EDMR signal at g-value ~2.005 at temperatures 30K and above, as shown in Fig. 2. We associate this signal to the presence of silicon dangling bonds based on the g-value. We also observed an EDMR signal at g-value ~1.998 at temperature ~5K. The origin of this signal is still being investigated. Thus, we show the proof of concept of minicells and EDMR measurements with which we now aim to study some of the unknown defects in silicon solar cell devices.

EDMR↗
DOE OSTI2023

Heat capacity of microgram oxide samples by fast scanning calorimetry

Quantitative scanning calorimetry on microgram-sized samples opens a broad, new range of opportunities for studying the thermodynamic properties of quantity-limited materials, including those produced under extreme conditions or found as rare accessory minerals in nature. We calibrated the Mettler Toledo Flash DSC 2+ calorimeter to obtain quantitative heat capacities in the range 200–350 °C, using samples weighing between 2 and 11.5 μg. Our technique is applied to a new set of oxide materials to which it has never been used before, without the need for melting, glass transitions, or phase transformations. Heat capacity data were obtained for silica in the high pressure stishovite (rutile) structure, dense post-stishovite glass, standard fused quartz, and for TiO2 rutile. These heat capacities agree within 5%–15% with the literature values reported for rutile, stishovite, and fused SiO2 glass. The heat capacity of post-stishovite glass, made by heating stishovite to 1000 °C, is a newly reported value. After accurate calibrations, measured heat capacities were then used to calculate masses for samples in the microgram range, a substantial improvement over measurement in conventional microbalances, which have uncertainties approaching 50%–100% for such small samples. Since the typical uncertainty of heat capacities measured on 10–100 mg samples in conventional differential scanning calorimetry is typically 7% (1%–5% with careful work), flash differential scanning calorimetry, using samples a factor of 1000 smaller, increases the uncertainty of heat capacity measurements by a factor of <3, opening the door for meaningful measurements on ultra-small, high-pressure samples and other quantity-limited materials.

Instruments & Instrumentation↗